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logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--9eb77636-7431-49d1-8268-d38ce9fd28a5/igs-energy%25403x.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"351","width":"624",":type":"snowflake-site/components/image"},"industry":"Fertigung","location":"Dublin, Ohio","stickyId":"sticky-1","cta":{"id":"cta","heapButtonClasses":["contact_sales"],"showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/de/contact/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"SALES-TEAM KONTAKTIEREN"},"workLoadItems":[],":type":"snowflake-site/components/customer-details-card"}},":itemsOrder":["customer_details_car"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"sticky-1",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-18786ce824","type":"heading4","lines":["Förderung von Kundenentscheidungen, die die Umwelt und den Geldbeutel schonen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color"},"text":{"id":"text-8d24579bb0","text":"\u003Cp\u003EIGS Energy setzt sich für eine grünere Zukunft ein. Mit dem Ziel, zuverlässige, erschwingliche und saubere Energie für alle zur Verfügung zu stellen, bietet IGS verschiedene nachhaltige Energielösungen an – darunter Strom aus erneuerbaren Energiequellen, CO\u003Csub\u003E2\u003C/sub\u003E-neutrales Erdgas und Solarkraft. Als Energieversorgungsunternehmen im Mittleren Westen der USA beliefert IGS über eine Million Kund:innen – von Unternehmen bis zu Privathaushalten – mit Strom und Erdgas. \u003C/p\u003E\n\u003Cp\u003E  \u003C/p\u003E\n\u003Cp\u003E„Daten tragen entscheidend zur Erreichung unseres Ziels bei: Sie helfen uns, unsere Angebote zuverlässiger und wettbewerbsfähiger für die Kundschaft zu machen“, sagt Dan Shah, Manager of Data Science bei IGS Energy. Als das alte On-Premises-System nicht mehr mit der riesigen täglich verarbeiteten Datenmenge mithalten konnte, begann IGS die Implementierung einer skalierbaren, stabilen Datenplattform anzustreben. Nun bildet Snowflake das Rückgrat seiner Dateninfrastruktur. So löst IGS mit KI/ML eine Vielzahl von Geschäftszielen – von der Bedarfsprognose bis zur Erkennung von Anomalien.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"quote_item_copy":{"id":"quote-item-38fca89457","alignment":"left","layout":"simple","quoteSourceName":"Dan Shah","quoteSourceTitle":"Manager of Data Science, IGS Energy","showQuoteIcon":false,"quote":"„Unser Modell zur Anomalieerkennung hilft dabei vorherzusagen, wann Dachsolaranlagen unterdurchschnittliche Leistung erbringen. Damit ist es uns möglich, ein besseres Kundenerlebnis zu bieten.“","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"storyHighlights",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-5811f0e686","type":"heading5","lines":["Highlights des Berichts"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-345c9d79e7","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cli\u003E\u003Cp\u003EKI-/ML-Prognosen mit um 75 % geringeren Kosten: Die Verlagerung von Hunderttausenden einzelnen Prognosemodellen in Databricks zu einem einheitlichen Modell in Snowflake verhalf IGS im Training zu Kosteneinsparungen von 75 % – ohne Abstriche bei der Genauigkeit. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EBessere Anomalieerkennung mit KI/ML zur Steigerung der Kundenzufriedenheit und zur Verringerung manueller Aufgaben: Durch die präzisere Erkennung von Problemen mit den Solarpaneelen der Kundschaft spart IGS Ressourcen und hilft Kund:innen dabei, mehr Wert aus ihren umweltfreundlichen Investitionen zu schöpfen. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003EKürzere Time-to-Value mit Streamlit: Dank der Anwendungsfreundlichkeit und intuitiven Oberfläche von Streamlit gelangen Vorschläge aller Beteiligten rasch an das Data-Science-Team von IGS. So kann das Team schneller iterieren und einen Mehrwert aufzeigen.\u003C/li\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"ui-background-05 snowflake-responsive-container-inner-padding-extra-small"},"title_v2_copy_copy":{"id":"title-v2-69e48b60cd","type":"heading4","lines":["Eine skalierbare Datenplattform, die bei täglichen Kaufentscheidungen unterstützt "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color"},"text_copy_copy":{"id":"text-4ac65545cf","text":"\u003Cp\u003EDaten sind also für das Ziel, das IGS sich gesteckt hat, von entscheidender Bedeutung. Und doch verließ sich das Unternehmen zuvor auf eine alte On-Premises-Dateninfrastruktur, die es dem Team erschwerte, ihre tägliche Arbeit präzise und effizient auszuführen. „Unsere Workflows waren alle miteinander verbunden und voneinander abhängig“, so Shah. „Brach ein Glied in der Kette, ließ es sich nur schwer abtrennen. Zudem war es schwierig, den Prozess zu verbessern.“\u003C/p\u003E\n\u003Cp\u003EDie genannten Herausforderungen waren besonders im Zusammenhang mit Nachfrageprognosen problematisch. Diese machen einen wesentlichen Bestandteil des IGS-Geschäfts aus und erfordern höchste Genauigkeit. „Wir treffen jeden Tag energiebezogene Kaufentscheidungen – Entscheidungen, die eine Prognose erfordern“, so Shah. Diese Transaktionen reichen von langfristigen Entscheidungen über den Stromeinkauf und -verkauf bis hin zu Handelsgeschäften auf dem „Day-Ahead-Energiemarkt“: Dort können Marktteilnehmende Strom für den Großhandel einen Tag vor dem Betriebstag kaufen oder verkaufen, um Preisschwankungen zu vermeiden.\u003C/p\u003E\n\u003Cp\u003EDie ML-basierten Prognosemodelle von IGS brauchen riesige Datenmengen – darunter wetterbezogene Informationen, Verlaufsdaten zum Stromverbrauch, Kontodetails und Vertragsdaten. Da die Großhandelspreise für Strom je nach Tageszeit variieren, muss das Unternehmen den Stromverbrauch pro Stunde für die Vertragsdauer der Kundschaft prognostizieren, welche sich über viele Jahre erstrecken kann. Die dabei erzeugten Daten können 40 bis 50 Milliarden Zeilen umfassen und ein Terabyte überschreiten. „Wenn im Vorfeld der Prognose etwas ausfällt, funktioniert der Prozess nicht und wir laufen Gefahr, ziemlich viel Geld zu verlieren“, erklärt Shah.\u003C/p\u003E\n\u003Cp\u003EAuf der Suche nach einer skalierbaren, flexiblen Lösung wandte sich IGS an Snowflake. „Die Data Cloud schien die kostengünstigste und schnellste Möglichkeit zu sein, diese riesigen Datenmengen zu speichern, abzufragen und zu nutzen“, sagt Shah. „Wir hatten den Anspruch, Daten nützlicher, aussagekräftiger und verwertbarer zu machen. Und Snowflake hat einen wichtigen Teil dazu beigetragen.“\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_v2_copy_copy_c":{"id":"title-v2-1ff720de5b","type":"heading4","lines":["Ein einheitliches Prognosemodell für 75 % Kosteneinsparungen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_copy":{"id":"text-ff17ca30a8","text":"\u003Cp\u003EMit Snowflake entwickelte Shahs Team die „nächste Generation“ seines Nachfrageprognosemodells. „Nachfrageprognosen sind in der Regel umso genauer, je tiefer man ins Detail geht“, so Shah. „Wir verwendeten jedoch ein Prognosemodell pro Konto, was aufgrund der hohen Anzahl an Kundenkonten mit sehr viel Aufwand verbunden war.“ IGS ging dazu über, statt einem Modell pro Kund:in nur ein zentrales, einheitliches Modell in Snowflake zu trainieren – eine Umstellung, die weniger Komplexität bei gleichbleibend hoher Genauigkeit sowie um 75 % verringerte Trainingskosten bedeutete.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"featured_stat_copy_c":{"id":"featured-stat-b1d4cc9e73","alignment":"left","image":{"id":"image","alt":"stat card image of windmills and solar panels in the sunset","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--462c964b-db16-47fa-8b35-93892771ca3c/igs-energy-stat-card-image.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"654","width":"1920",":type":"snowflake-site/components/image"},"statNumber":{"id":"stat-number","type":"display2","lines":["75 %"],":type":"snowflake-site/components/title-v2"},"statText":{"id":"stat-text","text":"\u003Cp\u003EKosteneinsparungen durch die Verlagerung von vielen Prognosemodellen auf nur ein in Snowflake zusammenlaufendes Modell\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},":type":"snowflake-site/components/featured-stat","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small snowflake-responsive-component-bottom-padding-small"},"text_copy_copy_63041":{"id":"text-a8a30c9a6c","text":"\u003Cp\u003E„Früher dauerte es eine halbe Stunde, all diese Modelle zu trainieren und Vorhersagen zu erzeugen“, sagt Shah. „Das zentralisierte Snowflake-Modell ist superschnell: Prognosen für Hunderttausende Kund:innen sind binnen weniger Minuten generiert. Diese Geschwindigkeit und Einfachheit ebnen unserem Unternehmen den Weg zu zusätzlichen Möglichkeiten wie Simulationen und Szenarioprognosen.“\u003C/p\u003E\n\u003Cp\u003EIm Snowflake-basierten Prognosemodell sieht Shah potenzielle Vorteile, die weit über IGS hinausgehen. „Wir glauben, dass dieser Ansatz außerhalb der Energiebranche funktionieren könnte, z. B. im Handel oder in der Supply Chain“, so Shah. „Er könnte eine Blaupause für jede Branche sein, in der die Anzahl der zu erstellenden Prognosen eine Herausforderung darstellt.“\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_v2_copy_copy_8_191437395":{"id":"title-v2-d249807227","type":"heading4","lines":["Ein besseres Modell zur Anomalieerkennung für zufriedenere Kund:innen und Data Scientists"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color"},"text_copy_copy_63041_306225306":{"id":"text-62483d50f7","text":"\u003Cp\u003ENeben erneuerbarer Energie über das Versorgungsnetz bietet IGS auch Dachsolaranlagen an. Nach der Installation der Anlage überwacht IGS ihre Stromerzeugung, um eine ordnungsgemäße Leistung sicherzustellen.\u003C/p\u003E\n\u003Cp\u003EZuvor ermittelte IGS alle Leistungsabweichungen manuell. Dabei wurden durchschnittliche Verlaufsdaten in der Stromerzeugung vom eigenen On-Premises-SQL-Server in eine Excel-Tabelle exportiert und diese dann mit der Produktion verglichen. Dieser manuelle Prozess war jedoch zeitintensiv und nicht so genau. „Dass diese Anomalien erkannt werden, ist von entscheidender Bedeutung, damit unsere Kundschaft mit den Solarmodulen zufrieden ist“, sagt Shah. „Vor Snowflake bestand jedoch das Risiko falsch-negativer Erkennungen – das heißt, wir übersahen Anomalien. Hinzu kam das Risiko falsch-positiver Ergebnisse, sodass wir rausfuhren, dann aber gar keine Probleme vorfanden.“\u003C/p\u003E\n\u003Cp\u003EShahs Team entschied sich, die Idee eines ML-gestützten Modells von Streamlit zu testen. Dabei werden Wetterdaten und Spezifikationen von Photovoltaikanlagen verwendet, um eine unterdurchschnittliche Leistung vorherzusagen. 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